Image + + + Classification result Numclass Convolution Max pooling Deconvolution Image Numclass Classification result Full connect CNN FCN

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.

[1]  J. Huynen.  Phenomenological theory of radar targets , 1970 .

[2]  L. Novak,et al.  Bayes classification of terrain cover using normalized polarimetric data , 1988 .

[3]  R. E. Carlson,et al.  An algorithm for monotone piecewise bicubic interpolation , 1989 .

[4]  E. Krogager.  New decomposition of the radar target scattering matrix , 1990 .

[5]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[6]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[7]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[8]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[9]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[10]  Thomas L. Ainsworth,et al.  Unsupervised classification using polarimetric decomposition and the complex Wishart classifier , 1999, IEEE Trans. Geosci. Remote. Sens..

[11]  M. Hellmann,et al.  Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[12]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  S. Fukuda,et al.  Support vector machine classification of land cover: application to polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[14]  Jong-Sen Lee,et al.  The use of fully polarimetric information for the fuzzy neural classification of SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jean-Claude Souyris,et al.  Support Vector Machine for Multifrequency SAR Polarimetric Data Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Lamei Zhang,et al.  Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features , 2010, EURASIP J. Adv. Signal Process..

[19]  Hao Chen,et al.  Unsupervised nonparametric classification of polarimetric SAR data using the K-nearest neighbor graph , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[20]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jakob J. van Zyl,et al.  Model-Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Nonnegative Eigenvalues , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[22]  J. Kong,et al.  Identification of Terrain Cover Using the Optimum Polarimetric Classifier , 2012 .

[23]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Lamei Zhang,et al.  Polarmetric SAR images classification based on sparse representation theory , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[25]  Shuang Wang,et al.  Multilayer feature learning for polarimetric synthetic radar data classification , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[26]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[27]  Lei Zhang,et al.  Projective dictionary pair learning for pattern classification , 2014, NIPS.

[28]  Lamei Zhang,et al.  Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Biao Hou,et al.  Multilevel Distribution Coding Model-Based Dictionary Learning for PolSAR Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[32]  Jian Cheng,et al.  Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features , 2015, Remote. Sens..

[33]  Yu Zhou,et al.  Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[34]  Hongwei Liu,et al.  Convolutional Neural Network With Data Augmentation for SAR Target Recognition , 2016, IEEE Geoscience and Remote Sensing Letters.

[35]  Jin Zhao,et al.  SAR Image Classification via Hierarchical Sparse Representation and Multisize Patch Features , 2016, IEEE Geoscience and Remote Sensing Letters.

[36]  Chiman Kwan,et al.  Bum scar detection using cloudy MODIS images via low-rank and sparsity-based models , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[37]  Shuai Yang,et al.  Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images , 2016, Remote. Sens..

[38]  Fang Liu,et al.  POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Chiman Kwan,et al.  A joint sparsity approach to tunnel activity monitoring using high resolution satellite images , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[40]  Fan Zhang,et al.  Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information , 2017, Remote. Sens..

[41]  Yuzhong Shen,et al.  Deep learning for effective detection of excavated soil related to illegal tunnel activities , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[42]  Kamran Azadet,et al.  Concurrent Dual-Band Digital Predistortion Using 2-D Lookup Tables With Bilinear Interpolation and Extrapolation: Direct Least Squares Coefficient Adaptation , 2017, IEEE Transactions on Microwave Theory and Techniques.

[43]  Jin Zhao,et al.  POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  Chiman Kwan,et al.  Application of Deep Belief Network to Land Cover Classification Using Hyperspectral Images , 2017, ISNN.

[45]  Chiman Kwan,et al.  A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover With Applications to Image Fusion, Pixel Clustering, and Anomaly Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Yongzhen Li,et al.  PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain , 2017, Remote. Sens..

[47]  Jin Zhao,et al.  Multilayer Projective Dictionary Pair Learning and Sparse Autoencoder for PolSAR Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[49]  Shuiping Gou,et al.  Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach , 2018, Remote. Sens..

[50]  Yan Wang,et al.  A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification , 2018, Remote. Sens..

[51]  Xin Xu,et al.  Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks , 2018, Sensors.

[52]  Chiman Kwan,et al.  Deep Learning with Synthetic Hyperspectral Images for Improved Soil Detection in Multispectral Imagery , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[53]  Junyu Gao,et al.  Embedding structured contour and location prior in siamesed fully convolutional networks for road detection , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[54]  Shunping Xiao,et al.  Polsar Target Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[55]  Si-Wei Chen,et al.  PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network , 2018, IEEE Geoscience and Remote Sensing Letters.